Gartner’s Data and Analytics Leader’s guide to Data Literacy had quoted, “By 2020, 50% of organizations will have insufficient AI and data literacy skills to realize real business value”. The other 50% will be equipped with smart algorithms, data literacy, and high resources for power computing enhancing the business value. Artificial Intelligence and machine learning will help the organizations unfold the Industry 4.0 revolution.
Business transformation digitally is going hand in hand with customer experience. Forbes prepared a list of statistical data that showed the importance of digital transformation and how that has impacted the growth of businesses. Some of the key findings were:
Sometimes organizations consider taking a leap on digital transformation however to attain that, a certain amount of maturity is a prerequisite. Artificial intelligence can act as a fuel in optimizing processes and derive insights. This is a clear thought that having access to the right data and analytics tools can highly enhance decision making. Machine learning facilitates the companies to build strategic models using predictive analysis that can fasten the testing cycle better than a human can do.
Accenture forecasted that the Industrial Internet of Things could contribute $10 trillion to the global economy by 2030. This report additionally explained that sensors, materials tracking mechanisms, 3D printing, automated product design, robotics, and wearables could help manufacturers reduce costs and increase productivity.
AB InBev, the world’s oldest most popular beer brand includes Budweiser, Corona, and Stella Artois was looking to improve operations at manufacturers and other companies. Pluto7 helped them in developing a prototype solution that optimized the beer filtration process with much greater accuracy, reducing costs, and increasing efficiency. All of this, without compromising in the taste of the beer. The filtration process was majorly used to remove the remaining yeast, proteins, and other extra elements to achieve the best, crusty taste of the beer. The complete filtration process has many unpredictable variables, such as fluids’ turbidity coming in and going out of the filter. This technology manipulating the variables is capable of only the basic logic using meters to monitor and react accordingly. It was after leveraging ML and AI solutions that the company could better predict and prevent potential issues during filtration. Therefore, an ML-enabled filtration process helped in delivering the best taste with notable cost efficiencies.
Not just this but the transportation sector has also started to implement the applications of artificial intelligence for critical tasks. Though the reliability still remains questionable for the general public for tasks like a self-driving vehicle. These fall under the category of typical challenges for the transportation industry like capacity problems, environment pollution, safety, and conservation of wasted energy.
In nutshell, artificial intelligence is a set of collective technologies that work together to let machines sense, learn, understand, and react to augment human activities. In today’s scenario data is present in vast cloud databases unless it’s extracted and processed. With artificial intelligence and machine learning capabilities, businesses can efficiently process billions of data points. The drastic rise in the use of AI technology has led to the automation of work making the organizations walk united with the new age business revolution.
We, at Pluto7, are enabling businesses with artificial intelligence and machine learning solutions driven by smart analytics. Are you still awaiting the change in your new business model to scale up with exponential growth? We are just a message away!
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Data is the new oil, and it’s real power comes from harnessing it to drive business innovation. But it is not that easy to be a data powered enterprise. According to IDC, these are the top three data challenges that are faced by enterprises:
Data volume is expected to go up from 33ZB in 2018 to 175ZB by 2025, that is a 530% increase. This increased data volume will skyrocket the data storage cost making this is an inevitable but expensive affair.
The size of the data alone is not the challenge but the nature of that data it is what will be difficult to manage. By 2025, more than 25% data will be streaming data. Real-time data is way more difficult to manage as compared to historical data.
The majority of data today is unstructured and only 1% of it is analyzed. With the amount of data growing by 5X in the next 5 years, it is going to become overwhelming to build an infrastructure that can drive maximum intelligence from the data.
Every lock has a key and the hope is not lost for enterprises. We at Pluto7, being awarded as Partner of the Year for Data and Analytics 2019 by Google Cloud , firmly believe that this five step structure built by Google Cloud can help a business become a data powered enterprise. These five steps are applicable to an enterprise irrespective of the vertical they are in, be it gaming or retail.
The traditional data warehouse implementations often hinder the business transformation since they are not easily scalable and increase the operational cost significantly. The first step is to simplify the operations and cut down the ownership cost to utilize the monetary resource for innovation. With Google Cloud, an enterprise can cut down its TCO easily by 50%.
Every department in a given enterprise has functions that influence one another but have unrelated data creating data silos. Think about different related departments like sales and marketing, their representative data silos adding to myriads of data looks structured but is scattered from an analytics point of view. In this step, the enterprise works to democratize data by breaking data silos and building a data culture while making sure that compliance terms are followed.
Location is a critical aspect for making contextual decisions. The streaming data enables faster decision making, by default all the SQL query in Google Cloud is streaming. It is important to make sure that the data is ingested, transformed/enriched and is streaming in the data warehouse. Stream data analytics on GCP enables real-time decision making.
This step onwards the enterprises can start harnessing the intelligence from their enriched data. Now, you can start generating prescriptive analytics. Rather than building those AI solutions in house you can always reach out to partners like Pluto7. Lot of APIs are already built by Google for enabling use cases like video classification, object tracking and a lot more.
Every business has its own set of challenges that can be solved with a set of use cases, few of them have to be custom-built and others can be bought as plug-and-play elements. While customizing AI-driven ML models consider these three criteria: how fast do you want it? ; How much effort would you want to invest? How much differentiation does this business use case have to offer? Answering these questions will help you prioritize and decide what you can build in house and what has to be outsourced.
Whether you are building an ML model on your own or purchasing from outside, the initial steps always have to be around building a robust data management system. At Pluto7 we are already helping fortune 500 enterprises democratizing their data and then generating valuable insights for their respective businesses.
Here is how our team is helping a leading data storage solutions provider in optimizing demand forecasting process: Link
LNutra is gaining a 360° view of their customer journey & activating marketing data like never before: Link
And the list goes on. If you want to know more about our customer success stories or if you are looking for some help on your data front, feel free to write us at firstname.lastname@example.org
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